\section{Related Works}

The most related article is a blog post about using PageRank to determine the
most powerful people on LinkedIn \cite{Puffelens2008}.  His thought being that
the person with the most networking skills is very influential.  People will
want to link with someone they look up to.  Similarly, we would be applying
PageRank to implicitly find the best looking web pages for a given topic.   A
more well-documented journal article related to our topic is about using
PageRank to determine where people's attention is in the blogosphere
\cite{Kirchhoff2007}.  Sites with higher PageRanks probably are more popular,
and in turn, their content is most likely popular.  A novel use of PageRank in
another domain is a project called CodeRank, which is used to determine popular
code modules in large software systems \cite{Neate2006}There is also a review of
PageRank's algorithms for trust computation \cite{Hussain2007}.  
Obviously, the second most valuable article is the Wikipedia entry for Bayes
Naïve classification.  Although we are not developing a traditional classifier
(that will determine for us whether a web pages components are good or bad), the
algorithm involved is useful to determine the probabilities of a web page of a
certain topic having the specified color scheme.
To learn about the background on web page templates, Gibson explains their
evolution \cite{1062763}.
Also, to gauge the impact of a web page based on looks, Reed does an analysis of
political websites \cite{1358925}.  
